The goal of this SBIR Phase I project is to implement and show the feasibility of software to assist in the process of identifying links among drug users and other hidden populations at risk for HIV infection and AIDS. In research on HIV infection network structure appears to be an important determinant, but practical difficulties in identifying individuals and tracking identity over time impede progress in this field. The project aims to produce effective and efficient software specifically designed to support identity elicitation for constructing social network models. Nominations by individuals of persons who are members of those individuals' social network are often difficult to link because the nominations are ambiguous and incomplete, for example the use of nicknames without standard information such as permanent residential address. When other individuals nominate these persons using a different nickname, linkage is problematic. The Phase I software will operate using a relational data base in which a number of potentially identifying attributes are recorded, including name and nicknames, dates and locations-of name usage, frequented physical locations ("hang outs"), gender, estimated age, physical features, etc. Estimation of social network structure can be based upon different combinations of matches to these attributes, with different degrees of certainty (to be empirically investigated in Phase II). Automatic estimation of standard network parameters will be included in the software. Testing to assess feasibility in Phase I will be through application of the software to existing data, using the software to investigate the relationships between risk prediction by social network position as a function of varying degrees of identification based upon linear combinations of identifying attributes. Network attributes assessed using the software are expected to predict HIV risk better using the software than not using it. One major aim in Phase II is to further refine this methodology by adding and studying software functions that will present tables of attributes in different ways to assist in helping subjects to recall nominations at repeated follow-up assessments in a systematic manner, with potential to improve reliability, validity, and sensitivity. Increasing the quality of longitudinal data will improve dynamic models of social network structure and change over time, which will assist in understanding change in social networks, important for guiding and assessing interventions. PROPOSED COMMERCIAL APPLICATIONS: The transmission of HIV is of major concern in the world today. Social network methods are an important tool in research on the spread of HIV, but complete software packages to support these methods do not exist as yet. The fundamental requirement is for input of accurate information about the relationships between individuals, but in populations in whom HIV rates are high this is generally a difficult task. Software that can effectively support this fundamental stage in applying social network methods has commercial potential in not only HIV research but other areas of application with similar problems and needs, for example drug abuse research. With this basic component, the addition to the software of other functions such as sophisticated estimation procedures and advanced graphical display will further enhance the value.